Why Is Machine Learning So Hot Now

Below is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of What Machine Learning Means for Company Analytics. The presenter is Abhishek Gupta, Chief Data Scientist at InetSoft.

Why is machine learning so hot now if I'm saying machine learning has been around since at least the 1950s? Why now? Why is it hyped now? I think there are a couple main reasons, one being just advances in technology and research. Of course, I mean computers are cheaper and more powerful. It was in 2006 that one of the first most important deep learning papers was published.

There have been fundamental changes in research and technology that have gotten us here. Another thing is now we have the ability to store a lot of data and cheaply, and in general machine learning algorithms do better with a lot of data. That's not always true, but in general that is true.

Another thing that's going on is there's business demand for machine learning, so things like text mining and recommendation systems. There is demand within business to pay for these kinds of activities. These have led to the rise of data science and the data scientist. I consider myself a data scientist.

Data scientists are likely when solving a problem to reach out to a machine learning algorithm as opposed to maybe just old fashion OLS regression or canonical discriminant analysis. What we're looking here at the screen is how the data scientist has long history going back to 1962, and we have some resources about this history. I just think it's important to keep in mind this history. This is something that's been building for a long time. A lot of factors have conspired to make it important and exciting now.

Data Scientist Persona

All right, so let's focus on this data scientist persona and how that is affecting machine learning in organizations. Well, we definitely believe that machine learning is being taken more seriously as data science and data scientists are taking more seriously. There's a lot to unpack here, but we've seen over the past few years sort of a segmentation of data scientist where there's different types of data scientists, different levels of data scientists.

This is important as you develop your machine learning strategy in your organization to realize that there are some data scientists who truly need to be given a lot of freedom, access to a lot of data, access to a lot of different tools, freedom within the organization, whereas other data scientists, more junior data scientists need more supervision and need more guidance and possibly need tools that can help them out more.

Another thing that we hear about a lot is the unicorn, and I think these unicorns do exist. They're hard to find. They're hard to keep, but if you can get one, good for you. If not, a lot of people are looking to build teams from preexisting resources and talent in their organization. I think that's a great thing to do. We're also looking at a Data Science Venn Diagram, and I think Another thing that we hear about a lot is the unicorn, and I think these unicorns do exist. They're hard to find. They're hard to keep, but if you can get one, good for you. If not, a lot of people are looking to build teams from preexisting resources and talent in their organization. I think that's a great thing to do. We're also looking at a Data Science Venn Diagram, and I think most organizations could dig up some programmers and statisticians and some business people and put them on a team together and that kind of equals a data scientist.

View a 2-minute demonstration of InetSoft's easy, agile, and robust BI software.

This idea of not struggling so hard to find the unicorn but looking within to develop data science capabilities from within, and machine learning capabilities from within, we think that's also a very successful strategy. Now, of course, there's a danger in letting people that don't have that much experience with mathematics starting to analyze data, and we would urge you to look for tools that provide bumper rails for those people, to keep them from getting into too much trouble. In general, we feel it's more important to bring people with business knowledge closer to the data.